Dr Jonathan Lancelot

Qualifications:

PhD in Cyber Defence specialising in Artificial Intelligence Dakota State University, United States — 2023 MSc in Cybersecurity and Statecraft Norwich University, United States — 2018 Bachelor’s in Policy and Governance American University, United States — 2004 Undergraduate studies included transferred coursework in engineering and sciences from Embry-Riddle Aeronautical University, including advanced mathematics, physics, chemistry, engineering, and flight training.

Position:

Senior Lecturer

Department:

School of Computing and Creative Technologies

Telephone:

+441179656261

Email:

Jonathan2.Lancelot@uwe.ac.uk

About me

Dr. Jonathan Lancelot is a Senior Lecturer in Cyber Security in the School of Computing and Creative Technologies. His research spans cyber defence, AI engineering, and robotics integration with a particular focus on real-world physical security applications for autonomous systems. 

Dr. Lancelot holds a PhD in Cyber Defence from Dakota State University and has authored original work on AI neural network computer vision models, stress testing electric vehicles automated systems, and cyberwarfare and technology's effect on statecraft. His cross-disciplinary interests includes philosophy, quantum neural networks, emergent computation theory, and the social impact of algorithmic communications platforms. He is currently developing AI avionics applications for advanced aerospace platforms and remote agents for autonomous robotic systems. 

Area of expertise

Research Artefacts and Technical Contributions

Development of experimental artificial intelligence architectures and sensing systems for constrained environments, including autonomous systems and space-based platforms.
• Convolutional Autoencoder Anomaly Detection System for Spacecraft Computer Vision
Developed a lightweight convolutional autoencoder architecture designed to detect anomalies within spacecraft visual sensing systems operating in autonomous environments.
• Firefly Swarm Salience Algorithm (FSSA)
Designed a swarm-inspired algorithm derived from glowworm swarm optimisation for identifying salient regions within dynamic visual fields to support anomaly detection and autonomous sensing.
• Binary Neural Network Architecture for Energy-Efficient Space Systems
Research into redesigned binary neural network structures capable of operating within energy-constrained sensing environments such as satellites and edge computing platforms.
• Edge AI Sensing Framework
Development of distributed sensing architectures integrating computer vision and local machine learning models for anomaly detection within cyber-physical infrastructure systems.

Selected Publications

Lancelot, J.F. (2024). Convolutional Autoencoder Neural Network Design Evaluation for an Anomaly Detection Subsystem in Autonomous Spacecraft Computer Vision Systems. PhD Dissertation.
Lancelot, J., Rimal, B., Dennis, E. (2023). Performance Evaluation of a Lane Correction Module Stress Test: A Field Test of Tesla Model 3. Future Internet.
Lancelot, J.F. Dynamic Fractional Stride Simulation in Binary Neural Networks for Energy Efficient Space Sensing and Autonomous Navigation. NATO Publication.
Lancelot, J.F. Cyber‑Diplomacy and the Rule of Engagement: Cyberwarfare in the Cyber Age. Journal of Cybersecurity.
Lancelot, J.F. The Deconstruction of Nation‑State Power and the Materialization of Cyber States. Cyberpolitik Journal.
Lancelot, J.F. Russia Today, Cyberterrorists Tomorrow: U.S. Failure to Prepare Democracy for Cyberspace. Journal of Digital Forensics, Security and Law.
Lancelot, J.F. Preparation for a Cybersecurity Apprenticeship (PCAP). EDSIGCON Proceedings.
Lancelot, J.F. Cyberwar to Kinetic War: 2020 Election and the Possibility of a Cyber‑Attack on Critical Infrastructure. Small Wars Journal.

Publications

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